Systematic Reviews and Meta Analysis
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Program Esnekliği ve Çevikliği Bağlamında Erken Uyarı Sistemleri: Sistematik Derleme

Year 2025, Volume: 15 Issue: 1, 209 - 233, 30.04.2025

Abstract

Sistematik derleme yöntemiyle yürütülen bu araştırma kapsamında, riskli davranışlar sergileyen üniversite öğrencilerinin belirlenebilmesi ve gerekli müdahalelerin gerçekleştirilebilmesi noktasında güncel ve eğitimsel bir perspektiften erken uyarı sistemlerinin incelenmesi ve tanıtılması hedeflenmiştir. Bu hedef doğrultusunda ilgili alan yazından yükseköğretimde erken uyarı sistemlerine ilişkin çeşitli uygulamaları içeren 2’si ulusal ve 29’u uluslararası olmak üzere toplam 31 araştırma incelenerek sekiz araştırma sorusuna yanıt aranmıştır. Araştırma sonucunda makale türünde yayımlanan araştırmaların genellikle 2000 yılından sonra gerçekleştirildiği gözlenirken, genel anlamda üniversite öğrencileriyle gerçekleştirilen bu araştırmalarda çoğunlukla nicel araştırma yöntemlerinin kullanıldığı gözlenmiştir. Öte yandan araştırmalarda veri kaynağı olarak en çok öğrenme yönetim sistemlerinden (Moodle, BookRoll, Öğrenme Akıllı Sistemi, Eğitim Yönetimi Sistemi ve Öğretim Asistanı Sistemi gibi kaynaklardan elde edilen tıklama sayısı, sistemde geçirilen toplam süre, kaynaklar, forumlarda gönderilen ve okunan mesajlar, ders notları, kitap okuma verileri vb.) yararlanıldığı saptanırken, verilerin analizi noktasında ise en çok veri madenciliği ve regresyon analizi yöntemlerinden yararlanıldığı saptanmıştır. Ayrıca araştırmalarda anahtar sözcük olarak genellikle erken uyarı sistem(ler)inin kullanıldığı belirlenmiştir. Sonuç olarak önemli konuların kapsamına dair açıklamaların yapılmasına ve daha fazla araştırma için önceliklerin belirlenmesine yönelik eşsiz fırsatların sunulduğu geleneksel ya da sistematik derleme türündeki çeşitli araştırmalar ışığında erken uyarı sistemlerinin yeterince tanıtılması gerektiği düşünülmektedir.

References

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Early Warning Systems in the Context of Curriculum Flexibility and Agility: Systematic Review

Year 2025, Volume: 15 Issue: 1, 209 - 233, 30.04.2025

Abstract

This systematic review aimed to examine and introduce early warning systems from both contemporary and educational perspectives to identify university students who exhibit risky behaviors and to implement the necessary interventions. To achieve this goal, a total of 31 studies—2 national and 29 international—were reviewed. These studies included various implementations of early warning systems at the higher education level, and 8 research questions were explored. The review revealed that studies on early warning systems have primarily been published after 2000. It was also found that quantitative research methods were most commonly used in these studies, which focused on university students. Additionally, learning management systems (such as Moodle, BookRoll, Learning Smart System, Teaching Assistant System, etc.) were the main data sources in these studies. The data from these systems included metrics such as the number of clicks, total time spent in the system, resources accessed, messages sent or read in forums, lecture notes, and book reading data. In terms of data analysis, data mining and regression analysis methods were predominantly used. Moreover, “early warning system” was commonly used as a keyword in the studies reviewed. In conclusion, it is suggested that the early warning system should be further promoted, based on the findings of this review, which offers valuable insights into key issues and establishes priorities for future research.

References

  • *Aguilar, S., Lonn, S., & Teasley, S. D. (2014, March, 24–28). Perceptions and use of an early warning system during a higher education transition program [Bildiri Sunumu]. Proceedings of the Fourth International Conference on Learning Analytics and Knowledge, Indianapolis.
  • *Akçapınar, G. (2014). Çevrim içi öğrenme ortamındaki etkileşim verilerine göre öğrencilerin akademik performanslarının veri madenciliği yaklaşımı ile modellenmesi [Yayımlanmamış doktora tezi]. Hacettepe Üniversitesi.
  • *Akçapınar, G., Altun, A., & Aşkar, P. (2019). Using learning analytics to develop early-warning system for at-risk students. International Journal of Educational Technology in Higher Education, 16(1), 1–20. https://doi.org/10.1186/s41239-019-0172-z
  • *Akçapınar, G., Hasnine, M. N., Majumdar, R., Flanagan, B., & Ogata, H. (2019). Developing an early-warning system for spotting at-risk students by using eBook interaction logs. Smart Learning Environments, 6(1), 1–15. https://doi.org/10.1186/s40561-019-0083-4
  • Alexandro, D. (2018). Aiming for success: Evaluating statistical and machine learning methods to predict high school student performance and improve early warning systems [Doktora Tezi, Connecticut Üniversitesi]. UCONN Library. https://opencommons.uconn.edu/dissertations/1982
  • Bakırtaş, D., & Nazlıoğlu, M. (2021). Okul terkinin maliyeti: Kamu gelirleri kapsamında Türkiye değerlendirmesi. Alanya Akademik Bakış, 5(2), 671–691. https://doi.org/10.29023/alanyaakademik.847747
  • *Bañeres, D., González, M. E., Roldán, A. E., & Cortadas, P. (2023). An early warning system to identify and intervene online dropout learners. International Journal of Educational Technology in Higher Education, 20(1), 1–25. https://doi.org/10.1186/s41239-022-00371-5
  • *Bañeres, D., Rodríguez, M. E., Roldán, A. E., & Karadeniz, A. (2020). An early warning system to detect at-risk students in online higher education. Applied Sciences, 10(13), 4427–4455. https://doi.org/10.3390/app10134427
  • Batool, S., Rashid, J., Nisar, M. W., Kim, J., Kwon, H. Y., & Hussain, A. (2023). Educational data mining to predict students’ academic performance: A survey study. Education and Information Technologies, 28(1), 905–971. https://doi.org/10.1007/s10639-022-11152-y
  • *Beck, H. P., & Davidson, W. D. (2001). Establishing an early warning system: Predicting low grades in college students from survey of academic orientations scores. Research in Higher Education, 42(6), 709–723. https://doi.org/10.1023/A:1012253527960
  • Bloland, H. G. (2005). Whatever happened to postmodernism in higher education?: No requiem in the new millennium. The Journal of Higher Education, 76(2), 121–150. https://doi.org/10.1080/00221546.2005.11778908
  • Brink, S., Carlsson, C. J., Enelund, M., Georgsson, F., Keller, E., Lyng, R., & McCartan, C. (2021, October 13–16). Curriculum agility: Responsive organization, dynamic content and flexible education [Bildiri Sunumu]. 2021 IEEE Frontiers in Education Conference (FIE), Lincoln.
  • Brink, S., Carlsson, C. J., Georgsson, F., Keller, E., Lyng, R., & McCartan, C. (2020, June 8). Assessing curriculum agility in a CDIO engineering education [Bildiri Sunumu]. 16th International CDIO Conference, Chalmers University of Technology.
  • Bruce, M., Bridgeland, J. M., Fox, J. H., & Balfanz, R. (2011). On track for success: The use of early warning indicator and intervention systems to build a grad nation. Johns Hopkins University, School of Education, Everyone Graduates Center. http://eric.ed.gov/?id=ED526421
  • Cano, A., & Leonard, J. D. (2019). Interpretable multiview early warning system adapted to underrepresented student populations. Transactions on Learning Technologies, 12(2), 198–211. https://doi.org/10.1109/TLT.2019.2911079
  • Cardona, T., Cudney, E. A., Hoerl, R., & Snyder, J. (2023). Data mining and machine learning retention models in higher education. Journal of College Student Retention: Research, Theory & Practice, 25(1), 51–75. https://doi.org/10.1177/1521025120964920
  • Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107, 1–7. https://doi.org/10.1016/j.chb.2018.06.032
  • Chung, J. Y., & Lee, S. (2019). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030
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There are 67 citations in total.

Details

Primary Language Turkish
Subjects Higher Education Studies (Other)
Journal Section Literature Review
Authors

Asil Derin Kılıç 0000-0003-2195-9889

Emine Er 0000-0003-4738-9749

Adnan Küçükoğlu 0000-0002-8522-258X

Early Pub Date April 30, 2025
Publication Date April 30, 2025
Submission Date February 26, 2024
Acceptance Date September 4, 2024
Published in Issue Year 2025 Volume: 15 Issue: 1

Cite

APA Derin Kılıç, A., Er, E., & Küçükoğlu, A. (2025). Program Esnekliği ve Çevikliği Bağlamında Erken Uyarı Sistemleri: Sistematik Derleme. Yükseköğretim Dergisi, 15(1), 209-233. https://doi.org/10.53478/yuksekogretim.1442815

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